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  4. Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems
 
conference paper

Meta Navigation Functions: Adaptive Associations for Coordination of Multi-Agent Systems

Macktoobian, Matin  
•
Duc, Guillaume Ferdinand
January 1, 2022
2022 American Control Conference (Acc)
American Control Conference (ACC)

In this paper, we introduce a new class of potential fields, i.e., meta navigation functions (MNFs) to coordinate multi-agent systems. Thanks to the MNF formulation, agents can contribute to each other's coordination via partial and/or total associations, contrary to traditional decentralized navigation functions (DNFs). In particular, agents may stimulate each other via their MNFs. Moreover, MNFs need to be confined which is a weaker condition compared to the Morse condition of DNFs. An MNF is composed of a confined function and an attraction kernel. The critical points of the former can be confined in a safe region around a target critical point. The collision-free trajectory of an agent and its associations to its peers are governed by a confined function before reaching its safe region. Then, the attraction kernel drives the agent to its target in the safe region. MNFs provide faster coordination compared to DNFs. We illustrate how MNFs may exhibit some social behaviors in the course of partial and total associations among agents. Our simulations verify the efficiency of MNFs to coordinate complex swarms of agents.

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Type
conference paper
DOI
10.23919/ACC53348.2022.9867570
Web of Science ID

WOS:000865458701133

Author(s)
Macktoobian, Matin  
Duc, Guillaume Ferdinand
Date Issued

2022-01-01

Publisher

IEEE

Publisher place

New York

Published in
2022 American Control Conference (Acc)
ISBN of the book

978-1-6654-5196-3

Start page

1921

End page

1926

Subjects

Automation & Control Systems

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASTRO  
Event nameEvent placeEvent date
American Control Conference (ACC)

Atlanta, GA

Jun 08-10, 2022

Available on Infoscience
November 21, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/192346
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